Facial image processing

ABSTRACT

In the described embodiment, methods and systems for processing facial image data for use in animation are described. In one embodiment, a system is provided that illuminates a face with illumination that is sufficient to enable the simultaneous capture of both structure data, e.g. a range or depth map, and reflectance properties, e.g. the diffuse reflectance of a subject&#39;s face. This captured information can then be used for various facial animation operations, among which are included expression recognition and expression transformation.

RELATED PATENT APPLICATIONS

This is a continuation of and claims priority to both U.S. patentapplication Ser. Nos. 10/837,424 and 09/652,600, the disclosures ofwhich are incorporated by reference herein.

BACKGROUND

The field of computer graphics involves rendering various objects sothat the objects can be displayed on a computer display for a user. Forexample, computer games typically involve computer graphics applicationsthat generate and render computer objects for display on a computermonitor or television. Modeling and rendering realistic images is acontinuing challenge for those in the computer graphics field. Oneparticularly challenging area within the computer graphics fieldpertains to the rendering of realistic facial images. As an example, aparticular computer graphics application may render a display of anindividual engaging in a conversation. Often times, the ultimatelyrendered image of this individual is very obviously a computer-renderedimage that greatly differs from a real individual.

Modeling and rendering realistic faces and facial expressions is aparticularly difficult task for two primary reasons. First, the humanskin has reflectance properties that are not well modeled by the variousshading models that are available for use. For example, the well-knownPhong model does not model human skin very well. Second, when renderingfacial expressions, the slightest deviation from what would be perceivedas “real” facial movement is perceived by even the casual viewer asbeing incorrect. While current facial motion capture systems can be usedto create quite convincing facial animation, the captured motion is muchless convincing, and frequently very strange, when applied to anotherface. For example, if a person provides a sampling of their facialmovements, then animating their specific facial movements is notdifficult considering that the face from which the movements originatedis the same face. Because of this, there will be movementcharacteristics that are the same or very similar between expressions.Translating this person's facial movements to another person's face,however, is not often times convincing because of, among other things,the inherent differences between the two faces (e.g. size and shape ofthe face).

Accordingly, this invention arose out of concerns associated withproviding improved systems and methods for modeling texture andreflectance of human skin. The invention also arose out of concernsassociated with providing systems and methods for reusing facial motioncapture data by transforming one person's facial motions into anotherperson's facial motions.

SUMMARY

The illustrated and described embodiments propose inventive techniquesfor capturing data that describes 3-dimensional (3-D) aspects of a face,transforming facial motion from one individual to another in a realisticmanner, and modeling skin reflectance.

In the described embodiment, a human subject is provided and multipledifferent light sources are utilized to illuminate the subject's face.One of the light sources is a structured light source that projects apattern onto the subject's face. This structured light source enablesone or more cameras to capture data that describes 3-D aspects of thesubject's face. Another light source is provided and is used toilluminate the subject's face. This other light source is sufficient toenable various reflectance properties of the subject's face to beascertained. The other light source is used in conjunction withpolarizing filters so that the specular component of the face'sreflectance is eliminated, i.e. only the diffuse component is capturedby the camera. The use of the multiple different light sources enablesboth structure and reflectance properties of a face to be ascertained atthe same time. By selecting the light sources carefully, for example, bymaking the light sources narrowband and using matching narrowbandfilters on the cameras, the influence of ambient sources of illuminationcan be eliminated.

Out of the described illumination process, two useful items areproduced—(1) a range map (or depth map) and (2) an image of the facethat does not have the structured light source pattern in it. A 3Dsurface is derived from the range map and surface normals to the 3Dsurface are computed. The processing of the range map to define the 3Dsurface can optionally include a filtering step in which a generic facetemplate is combined with the range map to reject undesirable noise. Thecomputed surface normals and the image of the face are then used toderive an albedo map. An albedo map is a special type of texture map inwhich each sample describes the diffuse reflectance of the surface of aface at a particular point on the surface. Accordingly, at this point inthe process, information has been ascertained that describes the3D-aspects of a face (i.e. the surface normals), and information thatdescribes the face's reflectance (i.e. the albedo map).

In one embodiment, the information or data that was produced in theillumination process is used to transform facial expressions of oneperson into facial expressions of another person. In this embodiment,the notion of a code book is introduced and used.

A code book contains data that describes many generic expressions ofanother person (person A). One goal is to take the code book expressionsand use them to transform the expressions of another person (person B).To do this, an inventive method uses person B to make a set of trainingexpressions. The training expressions consist of a set of expressionsthat are present in the code book. By using the training expressions andeach expression's corresponding code book expression, a transformationfunction is derived. The transformation function is then used to derivea set of synthetic expressions that should match the expressions ofperson B. That is, once the transformation function is derived, it isapplied to each of the expressions in the code book so that the codebook expressions match the expressions of person B. Hence, when a newexpression is received, e.g. from person B, that might not be in thetraining set, the synthesized code book expressions can be searched foran expression that best matches the expression of person B.

In another embodiment, a common face structure is defined that can beused to transform facial expressions and motion from one face toanother. In the described embodiment, the common face structurecomprises a coarse mesh structure or “base mesh” that defines asubdivision surface that is used as the basis for transforming theexpressions of one person into another. A common base mesh is used forall faces thereby establishing a correspondence between two or morefaces. Accordingly, this defines a structure that can be used to adaptface movements from one person to another. According to this embodiment,a technique is used to adapt the subdivision surface to the face modelof a subject. The inventive technique involves defining certain pointson the subdivision surface that are mapped directly to correspondingpoints on the face model. This is true for every possible different facemodel. By adding this constraint, the base mesh has a property in thatit fits different face models in the same way. In addition, theinventive algorithm utilizes a smoothing functional that is minimized toensure that there is a good correspondence between the base mesh and theface model.

In another embodiment, a reflectance processing technique is providedthat gives a measure of the reflectance of the surface of a subject'sface. To measure reflectance, the inventive technique separates thereflectance into its diffuse and specular components and focuses on thetreatment of the diffuse components.

To measure the diffuse component, an albedo map is first defined. Thealbedo map is defined by first providing a camera and a subject that isilluminated by multiple different light sources. The light sources arefiltered by polarizing filters that, in combination with a polarizingfilter placed in front of the camera, suppress specular reflection orprevent specular reflection from being recorded. A sequence of images istaken around the subject's head. Each individual image is processed toprovide an individual albedo map that corresponds to that image. All ofthe albedo maps for a particular subject are then combined to provide asingle albedo map for the subject's entire face.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the U.S. Patent and TrademarkOffice upon request and payment of the necessary fee.

FIG. 1 is a high level diagram of a general purpose computer that issuitable for use in implementing the described embodiments.

FIG. 2 is a schematic diagram of a system that can be utilized tocapture both structural information and reflectance information of asubject's face at the same time.

FIG. 3 is a flow diagram that describes an exemplary method forcapturing structural information and reflectance information inaccordance with the described embodiment.

FIG. 4 is a schematic diagram that illustrates an exemplary code bookand transformation function in accordance with the described embodiment.

FIG. 5 is a flow diagram that illustrates an expression transformationprocess in accordance with the described embodiment.

FIG. 6 is a high level diagram of an exemplary system in which certainprinciples of the described embodiments can be employed.

FIG. 7 is a collection of exemplary color plates that illustrate anexemplary expression transformation in accordance with the describedembodiment.

FIG. 8 is a color picture that illustrates the process of mapping thesame subdivision control mesh to a displaced subdivision surface fordifferent faces.

FIG. 9 is a color picture that illustrates exemplary constraints thatare utilized to enforce feature correspondence during surface fitting.

FIG. 10 is a flow diagram that describes steps in a surface fittingmethod in accordance with the described embodiment.

FIG. 11 is a schematic diagram of an exemplary system that can beemployed to build an albedo map for a face in accordance with thedescribed embodiment.

FIG. 12 is a color picture of an exemplary albedo map for twophotographs that are projected into texture space and corrected forlighting.

FIG. 13 is a color picture of an exemplary weighting function thatcorresponds to the FIG. 12 photographs.

FIG. 14 is a color picture of two full albedo maps for two differentdata sets.

FIG. 15 is a color diagram of the FIG. 14 albedo maps after editing.

FIGS. 16 a-c is a collection of color pictures of a face model that isrendered in different orientations and under different lightingconditions.

FIG. 17 is a flow diagram that describes steps in a method for creatingan albedo map in accordance with the described embodiment.

FIG. 18 is a flow diagram that describes steps in a method for computingan albedo for a single pixel in accordance with the describedembodiment.

DETAILED DESCRIPTION

Overview

Rendering realistic faces and facial expressions requires very goodmodels for the reflectance of skin and the motion of the face. Describedbelow are methods and techniques for modeling, animating, and renderinga face using measured data for geometry, motion, and reflectance thatrealistically reproduces the appearance of a particular person's faceand facial expressions. Because a complete model is built that includesgeometry and bi-directional reflectance, the face can be rendered underany illumination and viewing conditions. The described modeling systemsand methods create structured face models with correspondences acrossdifferent faces, which provide a foundation for a variety of facialanimation operations.

The inventive embodiments discussed below touch upon each of the partsof the face modeling process. To create a structured, consistentrepresentation of geometry that forms the basis for a face model andthat provides a foundation for many further face modeling and renderingoperations, inventive aspects extend previous surface fitting techniquesto allow a generic face to be conformed to different individual faces.To create a realistic reflectance model, the first known practical useof recent skin reflectance measurements is made. In addition, newlymeasured diffuse texture maps have been added using an improved texturecapture process. To animate a generic mesh, improved techniques are usedto produce surface shapes suitable for high quality rendering.

Exemplary Computer System

Preliminarily, FIG. 1 shows a general example of a desktop computer 130that can be used in accordance with the described embodiments. Variousnumbers of computers such as that shown can be used in the context of adistributed computing environment. These computers can be used to rendergraphics and process images in accordance with the description givenbelow.

Computer 130 includes one or more processors or processing units 132, asystem memory 134, and a bus 136 that couples various system componentsincluding the system memory 134 to processors 132. The bus 136represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. The system memory 134 includes read onlymemory (ROM) 138 and random access memory (RAM) 140. A basicinput/output system (BIOS) 142, containing the basic routines that helpto transfer information between elements within computer 130, such asduring start-up, is stored in ROM 138.

Computer 130 further includes a hard disk drive 144 for reading from andwriting to a hard disk (not shown), a magnetic disk drive 146 forreading from and writing to a removable magnetic disk 148, and anoptical disk drive 150 for reading from or writing to a removableoptical disk 152 such as a CD ROM or other optical media. The hard diskdrive 144, magnetic disk drive 146, and optical disk drive 150 areconnected to the bus 136 by an SCSI interface 154 or some otherappropriate peripheral interface. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data forcomputer 130. Although the exemplary environment described hereinemploys a hard disk, a removable magnetic disk 148 and a removableoptical disk 152, it should be appreciated by those skilled in the artthat other types of computer-readable media which can store data that isaccessible by a computer, such as magnetic cassettes, flash memorycards, digital video disks, random access memories (RAMs), read onlymemories (ROMs), and the like, may also be used in the exemplaryoperating environment.

A number of program modules may be stored on the hard disk 144, magneticdisk 148, optical disk 152, ROM 138, or RAM 140, including an operatingsystem 158, one or more application programs 160, other program modules162, and program data 164. A user may enter commands and informationinto computer 130 through input devices such as a keyboard 166 and apointing device 168. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, and one or morecameras, or the like. These and other input devices are connected to theprocessing unit 132 through an interface 170 that is coupled to the bus136. A monitor 172 or other type of display device is also connected tothe bus 136 via an interface, such as a video adapter 174. In additionto the monitor, personal computers typically include other peripheraloutput devices (not shown) such as speakers and printers.

Computer 130 commonly operates in a networked environment using logicalconnections to one or more remote computers, such as a remote computer176. The remote computer 176 may be another personal computer, a server,a router, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto computer 130, although only a memory storage device 178 has beenillustrated in FIG. 1. The logical connections depicted in FIG. 1include a local area network (LAN) 180 and a wide area network (WAN)182. Such networking environments are commonplace in offices,enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN networking environment, computer 130 is connected tothe local network 180 through a network interface or adapter 184. Whenused in a WAN networking environment, computer 130 typically includes amodem 186 or other means, such as a network interface, for establishingcommunications over the wide area network 182, such as the Internet. Themodem 186, which may be internal or external, is connected to the bus136 via a serial port interface 156. In a networked environment, programmodules depicted relative to the personal computer 130, or portionsthereof, may be stored in the remote memory storage device. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

Generally, the data processors of computer 130 are programmed by meansof instructions stored at different times in the variouscomputer-readable storage media of the computer. Programs and operatingsystems are typically distributed, for example, on floppy disks orCD-ROMs. From there, they are installed or loaded into the secondarymemory of a computer. At execution, they are loaded at least partiallyinto the computer's primary electronic memory. The invention describedherein includes these and other various types of computer-readablestorage media when such media contain instructions or programs forimplementing the steps described below in conjunction with amicroprocessor or other data processor. The invention also includes thecomputer itself when programmed according to the methods and techniquesdescribed below.

For purposes of illustration, programs and other executable programcomponents such as the operating system are illustrated herein asdiscrete blocks, although it is recognized that such programs andcomponents reside at various times in different storage components ofthe computer, and are executed by the data processor(s) of the computer.

Exemplary System for Capturing Structure and Properties of a FacialSurface

In the past, capturing systems have not been able to capture both facialstructure and reflectance properties of a whole face independently atthe same time. There are systems that, for example, use structured lightto capture the structure of the face—but these systems do not captureproperties of the face such as the reflectance. Similarly, there aresystems that capture reflectance of the face—but such systems do notcapture facial structure. The ability to capture facial structure andreflectance independently at the same time makes it possible to performadditional operations on collected data which is useful in various facerendering and animation operations. One particular example of anexemplary rendering operation is described below. It is to beunderstood, however, that the information or data that is produced as aresult of the system and method described below can be utilized invarious other areas. For example, areas of application include, withoutlimitation, recognition of faces for security, personal userinteraction, etc., building realistic face models for animation ingames, movies, etc., and allowing a user to easily capture his/her ownface for use in interactive entertainment or business communication.

FIG. 2 shows an exemplary system 200 that is suitable for use insimultaneously or contemporaneously capturing facial structure andreflectance properties of a subject's face. The system includes adata-capturing system in the form of one or more cameras, an exemplaryone of which is camera 202. Camera 202 can include a CCD image sensorand related circuitry for operating the array, reading images from it,converting the images to digital form, and communicating those images tothe computer. The system also includes a facial illumination system inthe form of multiple light sources or projectors. In the case wheremultiple cameras are used, they are genlocked to allow simultaneouscapture in time. In the illustrated example, two light sources 204, 206are utilized. Light source 204 desirably produces a structured patternthat is projected onto the subject's face. Light source 204 can bepositioned at any suitable location. This pattern enables structuralinformation or data pertaining to the 3-D shape of the subject's face tobe captured by camera 202. Any suitable light source can be used,although a pattern composed of light in the infrared region can beadvantageously employed. Light source 206 desirably produces light thatenables camera 202 to capture the diffuse component of the face'sreflectance property. Light source 206 can be positioned at any suitablelocation although it has been advantageously placed in line with thecamera's lens 202 a through, for example, beam splitting techniques.This light source could also be adapted so that it encircles the cameralens. This light source is selected so that the specular component ofthe reflectance is suppressed or eliminated. In the illustrated example,a linear polarizing filter is employed to produce polarizedillumination, and a second linear polarizer, which is orientedperpendicularly to the first, is placed in front of the lens 202 a sothat specular reflection from the face is not recorded by the camera.The above-described illumination system has been simulated using lightsources at different frequencies, e.g. corresponding to the red andgreen channels of the camera. Both of the channels can, however, be inthe infrared region. Additionally, by selecting the light sources to bein a narrow band (e.g. 780-880 nm), the influence of ambient light canbe eliminated. This property is only achieved when the camera is alsofiltered to a narrow band. Because the illumination from the lightsource is concentrated into a narrow band of wavelengths whereas theambient light is spread over a broad range of wavelengths, the lightfrom the source will overpower the ambient light for those particularwavelengths. The camera, which is filtered to record only thewavelengths emitted by the source, will therefore be relativelyunaffected by the ambient light. As a result, the camera will onlydetect the influence of the selected light sources on the subject.

Using the multiple different light sources, and in particular, aninfrared light source in combination with a polarized light source(which can be an infrared light source as well) enables the camera(which is configured with a complementary polarizer) to simultaneouslyor contemporaneously capture structural information or data about theface (from light source 204) and reflectance information or data aboutthe face (from light source 206) independently. The structuralinformation describes 3-dimensional aspects of the face while thereflectance information describes diffuse reflectance properties of theface. This information is then processed by a computerized imageprocessor, such as computer 208, to provide information or data that canbe used for further facial animation operations. In the example about tobe described, this information comprises 3-dimensional data (3D data)and an albedo map.

FIG. 3 is a flow diagram that describes steps in a method in accordancewith this described embodiment. The described method enables informationor data that pertains to structure and reflection properties of a faceto be collected and processed at the same time. Step 300 illuminates asubject's face with multiple different light sources. An exemplarysystem for implementing this step is shown in FIG. 2. It will beappreciated that although two exemplary light sources are utilized inthe given example, other numbers of light sources can conceivably beused. Step 302 measures range map data (depth map data) and image datafrom the illumination of step 300. That is, the illumination of step 300enables the camera to detect light reflectance that is utilized toprovide both range map data and image data (i.e. reflectance) that doesnot contain the structure light source pattern in it. The range map dataand image data are provided to computer 208 (FIG. 2) for processing. Atthis point, step 304 can optionally apply a generic face template to therange map data to reject various noise that can be associated with therange map data. A generic face template can be considered as a 3D filterthat rejects noise in the range map data. Generic face templates will beunderstood by those skilled in the art.

Step 306 uses the range map data to derive or compute a 3D surface. Anysuitable algorithm can be used and will be apparent to those skilled inthe art. Exemplary algorithms are described in the following papers:Turk & Levoy, Zippered Polygon Meshes from Range Images, SIGGRAPH 94; F.Bernardini, J. Mittleman, H. Rushmeier, C. Silva, and G. Taubin, TheBall-Pivoting Algorithm for Surface Reconstruction, Trans. Vis. Comp.Graph. 5:4 (1999). Step 308 then computes surface normal vectors(“surface normals”) to the 3D surface of step 306 using knownalgorithms. One way to accomplish this task is to compute the normals tothe triangles, average those triangle normals around each vertex to makevertex normals, and then interpolate the vertex normals across theinterior of each triangle. Other methods can, of course, be utilized.Step 310 then uses the computed surface normals of step 308 and theimage data of step 302 to derive an albedo map. An albedo is a specialtype of texture map in which each sample describes the diffusereflectance of the surface of a face at a particular point on the facialsurface. The derivation of an albedo map, given the information providedabove, will be understood by those skilled in the art. An exemplaryalgorithm is described in Marschner, Inverse Rendering for ComputerGraphics, PhD thesis, Cornell University, August 1998.

At this point, and as shown in FIG. 2, the illumination processing hasproduced 3D data that describes the structural features of a subject'sface and albedo map data that describes the diffuse reflectance of thefacial surface.

The above illumination processing can be used to extract the describedinformation, which can then be used for any suitable purpose. In oneparticularly advantageous embodiment, the extracted information isutilized to extract and recognize a subject's expressions. Thisinformation can then be used for expression transformation. In theinventive embodiment described just below, the expressions of one personcan be used to transform the expressions of another person in arealistic manner.

Expression Transformation Using a Code Book

In one expression transformation embodiment, the notion of a code bookis introduced and is utilized in the expression transformation operationthat is described below. FIG. 4 shows an exemplary code book 400 thatcontains many different expressions that have been captured from aperson. These expressions can be considered as generic expressions, orexpressions from a generic person rather than from a particularindividual. In the example, the expressions range from Expression 1through Expression N. Expression 1 could be, for example, a smile;Expression 2 could be a frown; Expression 3 could be an “angry”expression, and the like. The expressions that are contained in codebook 400 are mathematically described in terms of their geometry and canbe captured in any suitable way such as the process described directlyabove.

To effect expression transformation, a transformation function is firstderived using some of the expressions in code book 400. To derive thetransformation function, the notion of a training set of expressions 402is introduced. The expression training set 402 consists of a set ofexpressions that are provided by an individual other than the individualwhose expressions are described in the code book 400. The trainingexpressions of training set 402 are a subset of the code bookexpressions. That is, each expression in the training set corresponds toan expression in the code book 400. For example, the training set 402might consist of three expressions—Expression 1, Expression 2, andExpression 3, where the expressions are “smile”, “frown” and “angry”respectively. The goal of the transformation function is to take thegeometric deformations that are associated with expressions of thetraining set, and apply them to all of the expressions of the code book400 so that the code book expressions are realistic representations ofthe expressions. That is, consider that each person's face geometricallydeforms differently for any given expression. If one person's geometricfacial deformations for a given expression were to be simply applied toanother person's face for the purpose of rendering the expression, theface to which the deformations were applied would likely look verydistorted. This is a result of not only different facial geometries, butalso of differing facial deformations as between the faces. Accordingly,a transformation function is derived that gives the best transformationfrom one set of expressions to another.

Consider again FIG. 4 where a linear transformation processor 406 isshown. Transformation processor 406 can be implemented in any suitablehardware, software, firmware, or combination thereof. In the illustratedexample, the linear transformation processor 406 is implemented insoftware. The linear transformation processor receives as input thetraining set of expressions 402 and the corresponding code bookexpressions 404. The transformation processor processes the inputs toderive a transformation function 408. The transformation function 408can then be applied to all of the expressions in the code book 400 toprovide a synthesized set of expressions 410. The synthesized set ofexpressions represents expressions of the code book that have beenmanipulated by the geometric deformations associated with theexpressions of the person that provided the training set of expressions.

Facial displacements for identical expressions will not be the same ondifferent people for two reasons. First, the motion capture samplepoints (one particular example of how one could represent face movementsin this particular algorithm) will not precisely correspond because oferrors in placement. Second, head shape and size varies from person toperson.

The first mismatch can be overcome by resampling the motion capturedisplacement data for all faces at a fixed set of positions on a genericmesh. This is described below in more detail in the section entitled“Exemplary System and Method for Building a Face Model.” There, thefixed set of positions is referred to as the “standard samplepositions”. The resampling function is the mesh deformation function.The standard sample positions are the vertices of the face mesh thatcorrespond to the vertices of the generic mesh subdivided once.

The second mismatch requires transforming displacement data from oneface to another to compensate for changes in size and shape of the face.In the illustrated example, this is done by finding a small training setof corresponding expressions for the two data sets and then finding thebest linear transformation from one to another. As an example, considerthe following: In an experimental environment, emotion expressions weremanually labeled for 49 corresponding expressions including variousintensities of several expressions. For speech motion, 10,000 frameswere automatically aligned using time warping techniques.

Each expression is represented by a 3m-vector g that contains all of thex, y, and z displacements at the m standard sample positions. Given aset of n expression vectors for the face to be transformed,g_(a1 . . . n), and a corresponding set of vectors for the target face,g_(b1 . . . n), a set of linear predictors a_(j) is computed, one foreach coordinate of g_(a), by solving 3m linear least squares systems:a _(j) ·g _(ai) =g _(bi) [j],i=1 . . . n

In the illustrated example, only a small subset of the points of eachg_(aj) are used. Specifically, those points that share edges with thestandard sample point under consideration. In the mesh that was used,the average valence is about 6 so that the typical g_(aj) has 18elements. The resulting system is roughly n by 18.

The resulting linear system may be ill-conditioned, in which case thelinear predictors a_(j) do not generalize well. The spread of thesingular values is controlled when computing the pseudoinverse to solvefor the a_(j), which greatly improves generalization. All singularvalues less than ασ₁, where σ₁ is the largest singular value of thematrix and α=0.2 . . . 0.1 are zeroed out.

FIG. 5 is a flow diagram that describes steps in an expressiontransformation method in accordance with this described embodiment. Step500 provides a code book of expressions. An example of such a code bookis given above. Step 502 provides a training set of expressions.Typically, this training set is a set of expressions from a person whois different from the person who provided the code book expressions. Thetraining set of expressions can be captured in any suitable way. As anexample, the expressions can be captured using a system such as the oneillustrated in FIG. 2. After the training set of expressions isprovided, step 504 derives a transformation function using the trainingset and the code book. One exemplary way of accomplishing this task wasdescribed above. Other methods could, of course, be used withoutdeparting from the spirit and scope of the claimed subject matter. Forexample, one could use various kinds of nonlinear transformations suchas neural networks, or weighted sums of basis expressions. Once thetransformation function is derived, it is applied to all of theexpressions in the code book to provide or define a synthetic set ofexpressions that can then serve as a basis for subsequent facialanimation operations.

Exemplary Application

FIG. 6 shows a system 600 that illustrates but one example of how theexpression transformation process described above can be employed.System 600 includes a transmitter computing system or transmitter 602and a receiver computing system or receiver 604 connected forcommunication by a network 603 such as the Internet. Transmitter 602includes an illumination system 200 (FIG. 2) that is configured tocapture the expressions of a person as described in connection with FIG.2. Transmitter 602 also includes a code book 400, such as the onedescribed in connection with FIG. 4. It is assumed that the code bookhas been synthesized into a synthetic set of expressions as describedabove. That is, using a training set of expressions provided by theperson whose expressions illumination system 200 is configured tocapture, the code book has been processed to provide the synthesized setof expressions.

Receiver 604 includes a reconstruction module 606 that is configured toreconstruct facial images from data that is received from transmitter602. Receiver 604 also includes a code book 400 that is identical to thecode book that is included with the transmitter 602. Assume now, thatthe person located at transmitter 602 attempts to communicate with aperson located at receiver 604. As the person located at the transmitter602 moves their face to communicate, their facial expressions andmovement are captured and processed by the transmitter 602. Thisprocessing can include capturing their expressions and searching thesynthesized code book to find the nearest matching expression in thecode book. When a matching expression is found in the synthesized codebook, an index of that expression can be transmitted to receiver 604 andan animated face can be reconstructed using the reconstruction module606.

Exemplary Facial Transformation

FIG. 7 shows some effects of expression transfer in accordance with thedescribed embodiment. The pictures in the first row constitute asynthetic face of a first person (person A) that shows three differentexpressions. These pictures are the result of the captured facial motionof person A. Face motion for a second person (person B) was captured.The captured face motion for person B is shown in the third row. Here,the 3D motion data was captured by placing a number of colored dots onthe person's face and measuring the dots' movements when the person'sface was deformed, as will be understood by those of skill in the art.Motion data can, however, be captured by the systems and methodsdescribed above. Person B's captured motions were then used, asdescribed above, to transform the expressions of person A. The result ofthis operation is shown in the second row. The expressions in the threesets of pictures all correspond with one another. Notice how theexpressions in the first and second row look very similar even thoughthey were derived from two very different people, while the originalexpressions of the second person (row 3) look totally unlike those ofthe first and second rows.

Exemplary System and Methods for Building a Face Model

The model of a face that is needed to produce a realistic image has twoparts to it. The first part of the model relates to the geometry of theface (i.e. the shape of the surface of the face) while the second partof the model relates to the reflectance of the face (i.e. the color andreflective properties of the face). This section deals with the firstpart of that model—the geometry of the face.

The geometry of the face consists of a skin surface plus additionalsurfaces for the eyes. In the present example, the skin surface isderived from a laser range scan of the head and is represented by asubdivision surface with displacement maps. The eyes are a separatemodel that is aligned and merged with the skin surface to produce acomplete face model suitable for high quality rendering.

Mesh Fitting

The first step in building a face model is to create a subdivisionsurface that closely approximates the geometry measured by the laserrange scanner. In the illustrated example, the subdivision surfaces aredefined from a coarse triangle mesh using Loop's subdivision rules.Loop's subdivision rules are described in detail in Charles Loop, SmoothSubdivision Surfaces Based on Triangles, PhD thesis, University of Utah,August 1987. In addition, the subdivision surfaces are defined with theaddition of sharp edges similar to those described by Hoppe et al.,Piecewise smooth surface reconstruction, Computer Graphics (SIGGRAPH '94Proceedings) pps. 295-302, July 1994. Note that the non-regular creasemasks are not used. In addition, when subdividing an edge between a dartand a crease vertex, only the new edge adjacent the crease vertex ismarked as a sharp edge.

A single base mesh is used to define the subdivision surfaces for all ofthe face models, with only the vertex positions varying to adapt to theshape of each different face. In the illustrated example, a base meshhaving 227 vertices and 416 triangles was defined to have the generalshape of a face and to provide greater detail near the eyes and lips,where the most complex geometry and motion occur. The mouth opening is aboundary of the mesh, and is kept closed during the fitting process bytying together the positions of the corresponding vertices on the upperand lower lips. The base mesh has a few edges marked for sharpsubdivision rules that serve to create corners at the two sides of themouth opening and to provide a place for the sides of the nose to fold.Because the modified subdivision rules only introduce creases for chainsof at least three sharp edges, this model does not have creases in thesurface; only isolated vertices fail to have well-defined limit normals.

FIG. 8 shows an example of a coarse defined mesh (the center figure)that was used in accordance with this example. FIG. 8 visually shows howthe coarse mesh can be used to map the same subdivision control (coarse)mesh to a displaced subdivision surface for each face so that the resultis a natural correspondence from one face to another. This aspect isdiscussed in more detail below.

The process used to fit the subdivision surface to each face is based onan algorithm described by Hoppe et al. Piecewise smooth surfacereconstruction, Computer Graphics (SIGGRAPH '94 Proceedings) pps.295-302, July 1994. Hoppe's surface fitting method can essentially bedescribed as consisting of three phases: a topological type estimation(phase 1), a mesh optimization (phase 2), and a piecewise smooth surfaceoptimization (phase 3).

Phase 1 constructs a triangular mesh consisting of a relatively largenumber of triangles given an unorganized set of points on or near someunknown surface. This phase determines the topological type of thesurface and produces an initial estimate of geometry. Phase 2 startswith the output of phase 1 and reduces the number of triangles andimproves the fit to the data. The approach is to cast the problem asoptimization of an energy function that explicitly models the trade-offbetween the competing goals of concise representation and good fit. Thefree variables in the optimization procedure are the number of verticesin the mesh, their connectivity, and their positions. Phase 3 startswith the optimized mesh (a piecewise linear surface) that is produced inphase 2 and fits an accurate, concise piecewise smooth subdivisionsurface, again by optimizing an energy function that trades offconciseness and fit to the data. The phase 3 optimization varies thenumber of vertices in the control mesh, their connectivity, theirpositions, and the number and locations of sharp features. The automaticdetection and recovery of sharp features in the surface is an essentialpart of this phase.

In the present embodiment, processing differs from the approachdescribed in Hoppe et al. in a couple of ways. First, continuousoptimization is performed only over vertex positions, since we do notwant to alter the connectivity of the control mesh. Additionally,feature constraints are added as well as a smoothing term.

In the illustrated example, the fitting process minimizes thefunctional:E(v)=E _(d)(v,p)+λE _(s)(v)+μE _(c)(v)where v is a vector of all the vertex positions, and p is a vector ofall the data points from the range scanner. The subscripts on the threeterms stand for distance, shape, and constraints. The distancefunctional E_(d) measures the sum-squared distance from the rangescanner points to the subdivision surface:

${E_{d}( {v,p} )} = {\sum\limits_{i = 1}^{n_{p}}{a_{i}{{p_{i} - {\Pi( {v,p_{i}} )}}}^{2}}}$where p_(i) is the i^(th) range point and Π(v, p_(i)) is the projectionof that point onto the subdivision surface defined by the vertexpositions v. The weight a_(i) is a Boolean term that causes points forwhich the scanner's view direction at p_(i) is not consistent with thesurface normal at Π(v, p_(i)) to be ignored. Additionally, points arerejected that are farther than a certain distance from the surface:

$a_{i} = \{ \begin{matrix}1 & {{{if}\mspace{14mu}\langle {{s( p_{i} )},{n( {\Pi( {v,p_{i}} )} )}} \rangle} > {0\mspace{14mu}{and}\mspace{14mu}{{p_{i} - {\Pi( {v,p_{i}} )}}}} < d_{0}} \\0 & {otherwise}\end{matrix} $where s(p) is the direction toward the scanner's viewpoint at point pand n(x) is the outward-facing surface normal at point x.

The smoothness functional E_(s) encourages the control mesh to belocally planar. It measures the distance from each vertex to the averageof the neighboring vertices:

${E_{s}(v)} = {\sum\limits_{j = 1}^{n_{v}}{{v_{j} - {\frac{1}{\deg( v_{j} )}{\sum\limits_{i = 1}^{\deg{(v_{j})}}v_{{k\;}_{i}}}}}}^{2}}$

The vertices v_(ki) are the neighbors of v_(j).

The constraint functional E_(c) is simply the sum-squared distance froma set of constrained vertices to a set of corresponding targetpositions:

${E_{c}(v)} = {\sum\limits_{j = 1}^{n_{c}}{{{A_{c_{i}}v} - d_{i}}}^{2}}$

where A_(j) is the linear function that defines the limit position ofthe j^(th) vertex in terms of the control mesh, so the limit position ofvertex c_(i) is attached to the 3D point d_(i). The constraints couldinstead be enforced rigidly by a linear reparameterization of theoptimization variables, but it has been found that the soft-constraintapproach helps guide the iteration smoothly to a desirable localminimum. The constraints are chosen by the user to match the facialfeatures of the generic mesh to the corresponding features on theparticular face being fit. In the present example, approximately 25 to30 constraints are used, concentrating on the eyes, nose, and mouth.FIG. 9 shows the constraints on the subdivision control mesh at 900 andtheir corresponding points on a face model.

Minimizing E(v) is a nonlinear least-squares problem, because Π anda_(i) are not linear functions of v. However, such can be made a linearproblem by holding a_(i) constant and approximating Π(v, p_(i)) by afixed linear combination of control vertices. The fitting processtherefore proceeds as a sequence of linear least-squares problems withthe a_(i) and the projections of the p_(i) onto the surface beingrecomputed before each iteration. The subdivision limit surface isapproximated for these computations by the mesh at a particular level ofsubdivision. Fitting a face takes a small number of iterations (fewerthan 30), and the constraints are updated according to a simple scheduleas the iteration progresses, beginning with a high λ and low μ to guidethe optimization to a very smooth approximation of the face, andprogressing to a low λ and high μ so that the final solution fits thedata and the constraints closely. The computation time in practice isdominated by computing Π(v, p_(i)).

To produce the mesh for rendering, the surface is subdivided to thedesired level, producing a mesh that smoothly approximates the faceshape. A displacement is then computed for each vertex by intersectingthe line normal to the surface at that vertex with the triangulatedsurface defined by the original scan as described in Lee et al.,Displaced Subdivision Surfaces, (SIGGRAPH '00 Proceedings) July 2000.The resulting surface reproduces all the salient features of theoriginal scan in a mesh that has somewhat fewer triangles, since thebase mesh has more triangles in the more important regions of the face.The subdivision-based representation also provides a parameterization ofthe surface and a built-in set of multiresolution basis functionsdefined in that parameterization and, because of the feature constraintsused in the fitting, creates a natural correspondence across all facesthat are fit using this method. This structure is useful in many ways infacial animation.

FIG. 10 is a flow diagram that describes steps in a method for buildinga face model in accordance with this described embodiment. The methodcan be implemented in any suitable hardware, software, firmware orcombination thereof. In the present example, the method is implementedin software.

Step 1000 measures 3D data for one or more faces to providecorresponding face models. In the above example, the 3D data wasgenerated through the use of a laser range scan of the faces. It will beappreciated that any suitable method of providing the 3D data can beused. Step 1002 defines a generic face model that is to be used to fitto the one or more face models. It will be appreciated that the genericface model can advantageously be utilized to fit to many differentfaces. Accordingly, this constitutes an improvement over past methods inwhich this was not done. In the example described above, the genericface model comprises a mesh structure in the form of a coarse trianglemesh. The triangle mesh defines subdivision surfaces that closelyapproximate the geometry of the face. In the illustrated example, asingle base mesh is used to define the subdivision surfaces for all ofthe face models. Step 1004 selects specific points or constraints on thegeneric face model. These specific points or constraints are mappeddirectly to corresponding points that are marked on the face model. Themapping of these specific points takes place in the same manner for eachof the many different possible face models. Step 1006 fits the genericface model to the one or more face models. This step is implemented bymanipulating only the positions of the vertices to adapt to the shape ofeach different face. During the fitting process continuous optimizationis performed only over the vertex positions so that the connectivity ofthe mesh is not altered. In addition, the fitting process involvesmapping the specific points or constraints directly to the face model.In addition, a smoothing term is added and minimized so that the controlmesh is encouraged to be locally planar.

Adding Eyes

The displaced subdivision surface just described represents the shape ofthe facial skin surface quite well. There are, however, several otherfeatures that are desirable for a realistic face. The most important ofthese is the eyes. Since the laser range scanner does not capturesuitable information about the eyes, the mesh is augmented for renderingby adding separately modeled eyes. Unlike the rest of the face model,the eyes and their motions are not measured from a specific person, sothey do not necessarily reproduce the appearance of the real eyes.However, their presence and motion is critical to the overall appearanceof the face model.

Any suitable eye model can be used to model the eyes. In the illustratedexample, a commercial modeling package was used to build a modelconsisting of two parts. The first part is a model of the eyeball, andthe second part is a model of the skin surface around the eye, includingthe eyelids, orbit, and a portion of the surrounding face (this secondpart will be called the “orbit surface”). In order for the eye to becomepart of the overall face model, the orbit surface must be made to fitthe individual face being modeled and the two surfaces must be stitchedtogether. This is done in two steps: first the two meshes are warpedaccording to a weighting function defined on the orbit surface, so thatthe face and orbit are coincident where they overlap. Then the twosurfaces are cut with a pair of concentric ellipsoids and stitchedtogether into a single mesh.

Note that one of the advantageous features of the embodiments describedabove is that they provide a structure or framework that can be used totransform the expressions of one person into expressions of anotherperson. Because the fit of the generic face model to each individualface is constrained so that any given part of the generic model alwaysmaps to the same feature on every person's face—for example, the leftcorner of the mouth in the generic model always maps to the left cornerof the mouth on any person's face—the set of fitted face models providesa means for determining the point on any face that corresponds to aparticular point on a particular face. For example, suppose the motionof the left corner of the mouth on person A's face has been measured. Wecan use the fit of the generic model to face A to determine which pointof the generic model corresponds to that measured point, and then we canuse the fit of the generic model to face B to determine which point onB's face corresponds to the computed point on the generic model andtherefore also to the measured point on face A. This information isessential to transforming motion from one face to another because wehave to know which parts of the new face need to be moved to reproducethe motions we measured from a set of points on the measured face.

Moving the Face

The motions of the face are specified by the time-varying 3D positionsof a set of sample points on the face surface. When the face iscontrolled by motion-capture data these points are the markers on theface that are tracked by the motion capture system. The motions of thesepoints are used to control the face surface by way of a set of controlpoints that smoothly influence regions of the surface. Capturing facialmotion data can be done in any suitable way, as will be apparent tothose of skill in the art. In one specific example, facial motion wascaptured using the technique described in Guenter et al., Making Faces,Proceedings of SIGGRAPH 1998, pages 55-67, 1998.

Mesh Deformation

The face is animated by displacing each vertex w_(i) of the trianglemesh from its rest position according to a linear combination of thedisplacements of a set of control points q_(j). These control pointscorrespond one-to-one with the sample points p_(j) that describe themotion. The influence of each control point on the vertices falls offwith distance from the corresponding sample point, and where multiplecontrol points influence a vertex, their weights are normalized to sumto 1.

${{\Delta\; w_{i}} = {\frac{1}{\beta_{i}}{\sum\limits_{j}{\alpha_{ij}\Delta\; q_{j}}}}};{\alpha_{ij} = {h( {{{w_{i} - p_{j}}}/r} )}}$

where β_(i)=Σ_(k)α_(ik) if vertex i is influenced by multiple controlpoints and 1 otherwise. These weights are computed once, using the restpositions of the sample points and face mesh, so that moving the meshfor each frame is just a sparse matrix multiplication. For the weightingfunction, the following was used:h(x)=½+½ cos(πx).

Two types of exceptions to these weighting rules are made to handle theparticulars of animating a face. Vertices and control points near theeyes and mouth are tagged as “above” and “below,” and control pointsthat are, for example, above the mouth do not influence the motions ofvertices below the mouth. Also, a scalar texture map in the regionaround the eyes is used to weight the motions so that they tapersmoothly to zero at the eyelids. To move the face mesh according to aset of sample points, control point positions must be computed that willdeform the surface appropriately. Using the same weighting functionsdescribed above, we compute how the sample points move in response tothe control points. The result is a linear transformation: p=Aq.Therefore if at time t we want to achieve the sample positions p_(t), wecan use the control positions q_(t)=A⁻¹p_(t). However, the matrix A canbe ill-conditioned, so to avoid the undesirable surface shapes that arecaused by very large control point motions we compute A⁻¹ using the SVD(Singular Value Decomposition) and clamp the singular values of A⁻¹ at alimit M. In the illustrated example, M=1.5 was used. A standardreference that discusses SVD is Golub and Van Loan, Matrix Computations,3^(rd) edition, Johns Hopkins press, 1996.

Eye and Head Movement

In order to give the face a more lifelike appearance, procedurallygenerated motion is added to the eyes and separately captured rigid-bodymotion to the head as a whole. The eyeballs are rotated according to arandom sequence of fixation directions, moving smoothly from one to thenext. The eyelids are animated by rotating the vertices that define themabout an axis through the center of the eyeball, using weights definedon the eyelid mesh to ensure smooth deformations.

The rigid-body motion of the head is captured from the physical motionof a person's head by filming that motion while the person is wearing ahat marked with special machine-recognizable targets (the hat ispatterned closely on the one used by Marschner et al., Image-based BRDFmeasurement including human skin, Rendering Techniques '99 (Proceedingsof the Eurographics Workshop on Rendering), pps. 131-144, June 1998. Bytracking these targets in the video sequence, the rigid motion of thehead is computed, which is then applied to the head model for rendering.This setup, which requires simply a video camera, provides a convenientway to author head motion by demonstrating the desired actions.

Exemplary System and Methods for Modeling Reflectance

Rendering a realistic image of a face requires not just accurategeometry, but also accurate computation of light reflection from theskin. In the illustrated example, a physically-based Monte Carlo raytracer was used to render the face. Exemplary techniques are describedin Cook et al., Distribution Ray Tracing, Computer Graphics (SIGGRAPH'84 Proceedings), pps. 165-174, July 1984 and Shirley et al., MonteCarlo techniques for direct lighting calculations, Transactions onGraphics, 15(1):1-36, 1996. Doing so allows for the use of arbitraryBRDFs (bi-directional reflectance distribution functions) to correctlysimulate the appearance of the skin, which is not well approximated bysimple shading models. In addition, extended light sources are used,which, in rendering as in portrait photography, are needed to achieve apleasing image. Two important deviations from physical light transportare made for the sake of computational efficiency: diffuseinterreflection is disregarded, and the eyes are illuminated through thecornea without refraction.

In the illustrated example, a reflectance model for the skin is based onmeasurements of actual human faces. Exemplary techniques are describedin Marschner et al., Image based BRDF measurement including human skin,Rendering Techniques '99 (Proceedings of the Eurographics Workshop onRendering), pps. 131-144, June 1999. The measurements describe theaverage BRDFs of several subjects' foreheads and include fittedparameters for the BRDF model described in Lafortune et al., Non-linearapproximation of reflectance functions, Computer Graphics (SIGGRAPH '97Proceedings), pps. 117-126, August 1997. Accordingly, the measurementsprovide an excellent starting point for rendering a realistic face.However, the measurements need to be augmented to include some of thespatial variation observed in actual faces. This is achieved by startingwith the fit to the measured BRDF of one subject whose skin is similarto the skin of the face we rendered and dividing it into diffuse andspecular components. A texture map is then introduced to modulate each.

The texture map for the diffuse component, or the “albedo map”,modulates the diffuse reflectance according to measurements taken fromthe subjects' actual faces as described below. The specular component ismodulated by a scalar texture map to remove specularity from areas (suchas eyebrows and hair) that should not be rendered with skin reflectanceand to reduce specularity on the lower part of the face to approximatethe characteristics of facial skin. The result is a spatially varyingBRDF that is described at each point by a sum of the generalized cosinelobes of Lafortune et al., Non-linear approximation of reflectancefunctions, Computer Graphics (SIGGRAPH '97 Proceedings), pps. 117-126,August 1997.

Constructing the Albedo Map

In the illustrated and described embodiment, the albedo map, which mustdescribe the spatially varying reflectance due to diffuse reflection,was measured using a sequence of digital photographs of the face takenunder controlled illumination.

FIG. 11 shows an exemplary system that was utilized to capture thedigital photographs or images. In the illustrated system, a digitalcamera 1100 is provided and includes multiple light sources, exemplaryones of which are shown at 1102, 1104. Polarizing filters in the form ofperpendicular polarizers 1106, 1108, and 1110 are provided and cover thelight sources and the camera lens so that the specular reflections aresuppressed, thereby leaving only the diffuse component in the images. Inthe example, a subject wears a hat 1112 printed withmachine-recognizable targets to track head pose. Camera 1100 staysstationary while the subject rotates. The only illumination comes fromthe light sources 1102, 1104 at measured locations near the camera. Ablack backdrop is used to reduce indirect reflections from spilledlight.

Since the camera and light source locations are known, standard raytracing techniques can be used to compute the surface normal, theirradiance, the viewing direction, and the corresponding coordinates intexture space for each pixel in each image. Under the assumption thatideal Lambertian reflection is being observed, the Lambertianreflectance can be computed for a particular point in texture space fromthis information. This computation is repeated for every pixel in onephotograph which essentially amounts to projecting the image intotexture space and dividing by the computed irradiance due to the lightsources to obtain a map of the diffuse reflectance across the surface.Consider FIG. 12 in which two photographs are shown projected intotexture space and corrected for lighting. In practice the projection iscarried out by reverse mapping, with the outer loop iterating throughall the pixels in the texture map, and stochastic supersampling is usedto average over the area in the image that projects to a particulartexture pixel.

The albedo map from a single photograph only covers part of the surface,and the results are best at less grazing angles. Accordingly a weightedaverage of all the individual maps is computed to create a single albedomap for the entire face. The weighting function, a visual example ofwhich is given in FIG. 13, should be selected so that higher weights aregiven to pixels that are viewed and/or illuminated from directionsnearly normal to the surface, and should drop to zero well before eitherviewing or illumination becomes extremely grazing. In the illustratedexample, the following function was used (cos θ_(i) cos θ_(e)−c)^(p),with c=0.2 and p=4.

Before computing the albedo for a particular texture pixel, we verifythat the pixel is visible and suitably illuminated. Multiple rays aretraced from points on the pixel to points on the light source and to thecamera point, and the pixel is marked as having zero, partial, or fullvisibility and illumination. It is prudent to err on the large side whenestimating the size of the light source. Only albedos for pixels thatare fully visible, fully illuminated by at least one light source, andnot partially illuminated by any light source are computed. This ensuresthat partially occluded pixels and pixels that are in full-shadow orpenumbra regions are not used.

Some calibration is required to make these measurements meaningful. Thecamera's transfer curve was calibrated using the method described inDebevec et al., Recovering high dynamic range radiance maps fromphotographs, Computer Graphics (SIGGRAPH '97 Proceedings), pps. 369-378,August 1997. The light/camera system's flat-field response wascalibrated using a photograph of a large white card. The lens's focallength and distortion were calibrated using the technique described inZhang, A flexible new technique for camera calibration, Technical ReportMSR-TR-98-71, Microsoft Research, 1998. The absolute scale factor wasset using a reference sample of known reflectance. When image-to-imagevariation in light source intensity was a consideration, control wasprovided by including the reference sample in every image.

The texture maps that result from this process do a good job ofautomatically capturing the detailed variation in color across the face.In a few areas, however, the system cannot compute a reasonable result.Additionally, the strap used to hold the calibration hat in place isvisible. These problems are removed by using an image editing tool andfilling in blank areas with nearby texture or with uniform color.

FIGS. 14 and 15 show the raw and edited albedo maps for comparison. Theareas where the albedo map does not provide reasonable results can beseen where the surface is not observed well enough (e. g., under thechin) or is too intricately shaped to be correctly scanned andregistered with the images (e.g the ears). Neither of these types ofareas requires the texture from the albedo map for realisticappearance—the first because they are not prominently visible and thesecond because the geometry provides visual detail—so this editing hasrelatively little effect on the appearance of the final renderings.

FIGS. 16 a-c shows several different aspects of the face model, usingstill frames from the accompanying video. In the first row (FIG. 16 a),the face is shown from several angles to demonstrate that the albedo mapand measured BRDF realistically capture the distinctive appearance ofthe skin and its color variation over the entire face, viewed from anyangle. The second row (FIG. 16 b) shows the effects of rim and sidelighting, including strong specular reflections at grazing angles. Notethat the light source has the same intensity and is at the same distancefrom the face for all three images in this row. The directionalvariation in the reflectance leads to the familiar lighting effects seenin the renderings. In the third row (FIG. 16 c), expression deformationsare applied to the face to demonstrate that the face still looks naturalunder normal expression movement.

FIG. 17 is a flow diagram that describes steps in a method for creatingan albedo map in accordance with the described embodiment. The methodcan be implemented in any suitable hardware, software, firmware orcombination thereof. In the described embodiment, the method isimplemented in software in connection with a system such as the oneshown and described in FIG. 11.

Step 1700 provides one or more polarized light sources that can be usedto illuminate a subject. Exemplary light sources are described above. Inthe described embodiment, the light sources are selected so that thespecular component of the subject's facial reflectance is suppressed oreliminated. Step 1702 illuminates the subject's face with the lightsources. Step 1704 rotates the subject while a series of digitalphotographs or images are taken. Step 1706 computes surface normals,irradiance, viewing direction and coordinates in texture space for eachpixel in the texture map. The computations can be done using knownalgorithms. Step 1708 computes the Lambertian reflectance for aparticular pixel in the texture space for the image. This provides analbedo for the pixel. Step 1710 determines whether there are anyadditional pixels in the albedo map. If there are, step 1712 gets thenext pixel and returns to step 1708. If there are no additional pixelsin the albedo map, step 1714 ascertains whether there are any additionaldigital images. If there are additional digital images, step 1716 getsthe next digital image and returns to step 1706. If there are noadditional digital images, then step 1718 computes a weighted average ofthe individual albedo maps for each image to create a single albedo mapfor the entire face. One specific example of how this weighted averageprocessing takes place is given above and described in Marschner,Inverse Rendering for Computer Graphics, PhD thesis, Cornell University,August 1998.

FIG. 18 is a flow diagram that describes steps in a method for computingan is albedo for a single pixel. This method can be implemented in anysuitable hardware, software, firmware or combination thereof. In thedescribed embodiment, the method is implemented in software. Step 1800determines, for a given pixel, whether the pixel is fully visible. Ifthe pixel is not fully visible, then an albedo for the pixel is notcomputed (step 1804). If the pixel is fully visible, step 1802determines whether the pixel is fully illuminated by at least one lightsource. If the pixel is not fully illuminated by at least one lightsource, then an albedo for the pixel is not computed (step 1804). If thepixel is fully illuminated by at least one light source, then step 1806determines whether the pixel is partially illuminated by any lightsource. If so, then an albedo is not computed for the pixel. If thepixel is not partially illuminated by any light source, then step 1808computes an albedo and a weight for the pixel. The weights are laterused in averaging together individual maps. Hence, as discussed above,albedos are computed only for pixels that are fully visible, fullyilluminated by at least one light source, and not partially illuminatedby any light source. This ensures that partially occluded pixels andpixels that are in full-shadow or penumbra are not used.

Conclusion

The embodiments described above provide systems and methods that addressthe challenge of modeling and rendering faces to the high standard ofrealism that must be met before an image as familiar as a human face canappear believable. The philosophy of the approach is to use measurementswhenever possible so that the face model actually resembles a real face.The geometry of the face is represented by a displacement-mappedsubdivision surface that has consistent connectivity and correspondenceacross different faces. The reflectance comes from previous BRDFmeasurements of human skin together with new measurements that combineseveral views into a single illumination-corrected texture map fordiffuse reflectance. The motion comes from previously described motioncapture technique and is applied to the face model using an improveddeformation method that produces motions suitable for shaded surfaces.The realism of the renderings is greatly enhanced by using the geometry,motion, and reflectance of real faces in a physically-based renderer.

Although the invention has been described in language specific tostructural features and/or methodological steps, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or steps described. Rather, thespecific features and steps are disclosed as preferred forms ofimplementing the claimed invention.

1. A computer-implemented facial image-processing method comprising:illuminating a face with multiple different light sourcessimultaneously, wherein at least one of the light sources is polarized;measuring range map data from said illuminating; measuring image datafrom said illuminating; applying a generic face template to the rangemap data to reject noise that is associated with the range map data;deriving a 3-dimensional surface from the range map data; computingsurface normals to the 3-dimensional surface; and processing the surfacenormals and the image data to derive an albedo map.
 2. The method ofclaim 1, wherein all of the light sources are polarized.
 3. The methodof claim 1 further comprising prior to deriving the 3-dimensionalsurface, filtering the range map data.
 4. A computer-implemented facialimage-processing method comprising: illuminating a face withillumination, wherein the illumination comprises: a first light sourcethat is polarized and selected so that specularly-suppressed reflectiveproperties of the face can be ascertained; and a second light sourcethat projects a structured pattern onto the face, while simultaneouslyilluminating the face with the first light source, wherein the secondlight source projects the structured pattern on the face from whichstructure data can be ascertained; and contemporaneously capturingstructure data describing the face's dimensional shape and structure andreflectance data describing reflectance properties of the face from theillumination, whereby the computer processes the structure data and thereflectance data to provide range map data, the range map data beingfiltered by applying a generic face template to reject noise in therange map data.
 5. The method of claim 4, wherein said illuminatingcomprises using additional light sources.
 6. The method of claim 4,wherein the illuminating further comprises an infrared light source. 7.The method of claim 4, wherein the first and second light sourcescomprise infrared light sources.
 8. The method of claim 4, wherein saidilluminating comprises using multiple polarized light sources.
 9. Themethod of claim 4, wherein said illuminating comprises illuminating theface with light sources at different frequencies.
 10. The method ofclaim 4, wherein said capturing comprises using a camera having apolarizer that suppresses specularly-reflected light so that diffusecomponent reflection data is captured.
 11. The method of claim 10,wherein the second light source comprises an infrared light source. 12.The method of claim 4, wherein said illuminating comprises illuminatingthe face with multiple narrow-band light sources.
 13. Acomputer-implemented facial image-processing method comprising:illuminating a face with a first polarized light source that is selectedso that specularly-suppressed reflective properties of the face can beascertained; illuminating the face with a second structured light sourcethat projects a pattern onto the face, while simultaneously illuminatingthe face with the first polarized light source; capturing bothspecularly-suppressed reflection data and structure data from thesimultaneous illumination, whereby the computer processes the structuredata and the specularly-suppressed reflection data to provide range mapdata, the range map data being filtered by applying a generic facetemplate to reject noise in the range map data.
 14. The method of claim13, wherein the light sources provide light at different frequencies.15. The method of claim 13, wherein the light sources provide infraredlight.
 16. The method of claim 13 further comprising processing thecaptured data to provide both (a) data that describes dimensionalaspects of the face and (b) data that describes diffuse reflectiveproperties of the face.
 17. The method of claim 16, wherein the datathat describes the diffuse reflective properties of the face comprisesan albedo map.